Our discussion with Lilian Monahan from Fujifilm proved extremely popular. Many people tuned in to discover how to efficiently use their data to enable continuous process improvement. The conversation illustrated how important efficient access to data is for these initiatives and how using the full range of available data, along with the right tools, allows scientists and engineers to improve the understanding and communication of their findings.
Unsurprisingly, the viewers asked many great questions, but due to time constraints, we could not cover them all during the event. After review, we realized these questions fell into four distinct topics that would interest the JMP community. For clarity, we summarized answers to the questions in a short overview for each.
Data Accessibility
Most of the questions on data accessibility came down to three things: how to collect your data, how to make it available to the right people, and ultimately how to process large volumes of data once you have it.
If you’re in a situation where the data you require isn’t obviously available, it’s often best to start with a conversation within your organization. While this sounds obvious and simple, these small conversations often help locate most of the data you require for the immediate task and initiate a broader conversation about how and where data is stored and used. These broader conversations and internal success stories can be used to build momentum around data analytics in your organization.
For many organizations, there can be concerns that it could be too challenging to bring all their data together or that only some of the data is readily available. Fortunately, JMP provides organizations with a platform to bring together the data they have for rapid, interactive exploration. This reveals answers in the data efficiently, allowing critical decisions to be made in a timely manner.
Simple Automation Using The Workflow Builder
In our conversation, Lilian discussed how Fujifilm simplified and standardized data access using an add-in, and naturally, many viewers wanted to understand more. In their case, they implemented a scripted solution using JSL, but as illustrated in the demonstrations, the introduction of the Workflow Builder in JMP 17 allows these automations to be created without coding. Unsurprisingly, this was a topic that generated much interest as a simple route that reduces the time it takes to get to insight is always welcome.
The Workflow Builder platform provides a no-code route to automation by recording the steps to perform your analyses. This means that you only need to perform your data import, preparation, and analysis steps once to generate a workflow that can be used to repeat this work whenever needed. Once your process has a workflow, it is simple to extend its utility by recording new steps and reordering existing ones. Once you’re happy with the process, it can be shared easily with your colleagues as a workflow or used to generate the corresponding JSL script to create a JMP add-In. All of this allows you to standardize and automate your routine tasks, without needing to do any coding.
Organizational Change
Following the discussion, we had a few questions that focused on different outputs but came to the same question: “Data analytics is a good idea in principle, but how do I convince people across the organization to use the output of this work?” Usually, it is most effective to use your own data to illustrate the power of these approaches to your colleagues, as these will be the most compelling stories.
For process monitoring, for example, you may want to move from only monitoring specification events to monitoring potential issues using control chart rules. On the surface, this can appear to be a fundamental change for someone not used to this approach. However if that discussion is accompanied with an example that illustrates how these SPC approaches can identify issues before that process has gone out of specification, it gives credibility to your message and creates an incentive to change.
By illustrating how simple it can be to use your data with the interactive tools in JMP to develop better and more consistent processes, you can create a compelling case for data analytics in your organization and the value of fully understanding what your data is telling you.
Training and Upskilling Scientists and Engineers
Several questions asked how best to upskill people to take advantage of data analytics. Alongside more general questions in this area, a number focused on some of the Statistical Process Control (SPC) methods that we discussed during the session.
The best place to start for both questions is the resources that JMP makes available. As we discussed during the webinar, the Statistical Thinking for Industrial Problem Solving (STIPS) course is a brilliant resource that sets out both the why and, more importantly, the how of data analytics across a range of topics, with each of these topics having further resources available in the Learn JMP section of the JMP User Community. For more detailed resources on SPC, a complimentary e-learning course on SPC by JMP Education is available.
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